Venugopal Balijepally’s research while affiliated with Oakland University and other places

This research explores extant research on artificial intelligence (AI) and big data published in the premier Information Systems (IS) journals over a period of 26 years (1997-2022), and it uses the techniques of citation analysis, bibliographic coupling, and co-word analysis. Citation analysis results reveal IS as the most cited reference discipline , followed by general business, organization science, and marketing. Two major topical clusters have been identified-problem domain-specific AI (e.g., predictive analytics, machine learning algorithms, and text mining) and organizational-specific AI (e.g., big data capabilities, firm performance, agility, and strategy). Co-word analysis revealed a gradual shift of scholarly interest from problem-domain-specific AI toward organizational-specific AI. Using the citation data, the most influential (cited) authors, (cited) articles, journals, institutions, and countries are identified. Gaps in extant research and future research paths are discussed.

Hypercoagulability is a recognized feature in SARS-CoV-2 infection. There exists a need for a dedicated risk assessment model (RAM) that can risk-stratify hospitalized COVID-19 patients for venous thromboembolism (VTE) and guide anticoagulation. We aimed to build a simple clinical model to predict VTE in COVID-19 patients. This large-cohort, retrospective study included adult patients admitted to four hospitals with PCR-confirmed SARS-CoV-2 infection. Model training was performed on 3531 patients hospitalized between March and December 2020 and validated on 2508 patients hospitalized between January and September 2021. Diagnosis of VTE was defined as acute deep vein thrombosis (DVT) or pulmonary embolism (PE). The novel RAM was based on commonly available parameters at hospital admission. LASSO regression and logistic regression were performed, risk scores were assigned to the significant variables, and cutoffs were derived. Seven variables with assigned scores were delineated as: DVT History = 2; High D-Dimer (>500–2000 ng/mL) = 2; Very High D-Dimer (>2000 ng/mL) = 5; PE History = 2; Low Albumin (<3.5 g/dL) = 1; Systolic Blood Pressure <120 mmHg = 1, Tachycardia (heart rate >100 bpm) = 1. The model had a sensitivity of 83% and specificity of 53%. This simple, robust clinical tool can help individualize thromboprophylaxis for COVID-19 patients based on their VTE risk category.

Abstract Background Patients with COVID-19 infection are commonly reported to have an increased risk of venous thrombosis. The choice of anti-thrombotic agents and doses are currently being studied in randomized controlled trials and retrospective studies. There exists a need for individualized risk stratification of venous thromboembolism (VTE) to assist clinicians in decision-making on anticoagulation. We sought to identify the risk factors of VTE in COVID-19 patients, which could help physicians in the prevention, early identification, and management of VTE in hospitalized COVID-19 patients and improve clinical outcomes in these patients. Method This is a multicenter, retrospective database of four main health systems in Southeast Michigan, United States. We compiled comprehensive data for adult COVID-19 patients who were admitted between 1st March 2020 and 31st December 2020. Four models, including the random forest, multiple logistic regression, multilinear regression, and decision trees, were built on the primary outcome of in-hospital acute deep vein thrombosis (DVT) and pulmonary embolism (PE) and tested for performance. The study also reported hospital length of stay (LOS) and intensive care unit (ICU) LOS in the VTE and the non-VTE patients. Four models were assessed using the area under the receiver operating characteristic curve and confusion matrix. Results The cohort included 3531 admissions, 3526 had discharge diagnoses, and 6.68% of patients developed acute VTE (N = 236). VTE group had a longer hospital and ICU LOS than the non-VTE group (hospital LOS 12.2 days vs. 8.8 days, p

Software development is a problem-solving activity, where ideas are combined in complex ways to create a software product that embodies new knowledge. In this endeavor, software developers constantly look for actionable knowledge to help solve the problem at hand. While knowledge management efforts in the software development domain traditionally involved technical initiatives such as knowledge repositories, experience factories, and lessons-to-learn databases, there is a growing appreciation in the software community of the role of developers' personal knowledge networks in software development. However, research is scarce on the nature of these networks, the knowledge resources accessed from these networks, and the differences, if any, between developers of different experience levels. This research seeks to fill this void. Based on a case study in a software development organization, this research explores the nature of knowledge networks of developers from a social capital perspective. Specifically, it examines the structural and relational dimensions of developers' knowledge networks, identifies the specific actionable knowledge resources accessed from these networks, and explores how entry-level and more experienced developers differ along these dimensions. The findings from the qualitative analysis, backed by limited quantitative analysis of the case study data underpin the discussion, implications for practice and future research directions.

Agile Development Methods, considered as an alternative to the traditional plan-based methods, have received much attention since their inception. These practices have evolved and developed over time, culminating in 2001 with the Agile Manifesto. Since that time, preferred methodologies, implementations, and best practices have continued to evolve with a focus on doing what works best for the individual company or project. However, the concept of agility in software development has remained quite nebulous, lacking in clarity particularly about its underlying dimensions. In this research the authors conceive agility in terms of four distinct dimensions. Drawing from the theoretical perspective of holographic organization, they develop a model explaining how each of these underlying dimensions of agility contributes to project value in software teams. The authors test the model using survey data collected from industry practitioners and discuss findings.

Background Patients with COVID-19 infection are commonly reported to have an increased risk of venous thrombosis. The choice of anti-thrombotic agents and doses in COVID-19 patients are currently being studied in randomized controlled trials and retrospective studies. There exists a need for individualized risk stratification of venous thromboembolism (VTE) to assist clinicians in decision-making on anticoagulation. We sought to identify the risk factors of VTE in COVID-19 patients, which could help physicians in prevention, early identification and management of VTE in hospitalized COVID-19 patients and improve clinical outcomes in these patients. Methods This is a multicenter, retrospective database of four main health systems in southeast Michigan, United States. We compiled comprehensive data for adult COVID-19 patients who were admitted between 1st March 2020 and 31st December 2020. Four models including random forest, multiple logistic regression, multiple linear regression, and decision trees were built on the primary outcome of in-hospital acute deep vein thrombosis and pulmonary embolism and tested for performance. The study also reported hospital length of stay (LOS) and intensive care unit (ICU) LOS in the VTE and the non-VTE patients. Four models were assessed using area under the receiver operating characteristic curve and confusion matrix. Results The cohort included 3531 admissions, 3526 had discharge diagnosis, and 6.68% patients developed acute VTE (N=236). VTE group had a longer hospital and ICU LOS than non-VTE group (hospital LOS 12.2 days vs 8.8 days, p<0.001; ICU LOS 3.8 days vs 1.9 days, p<0.001). 9.8% patients in VTE group required more advanced oxygen support, compared to 2.7% patients in non-VTE group (p<0.001). Among all four models, the random forest model had the best performance. The model suggested that blood pressure, electrolytes, renal function, hepatic enzymes and inflammatory markers were predictors of in-hospital VTE in COVID-19 patients. Conclusions and Relevance Patients with COVID-19 have increased risk for VTE, and patients who developed VTE had a prolonged hospital and ICU stay. This random forest prediction model for VTE in COVID-19 patients identifies predictors which could aid physicians in making clinical judgment on empirical dosages of anticoagulation.

Software development is a problem-solving activity, where ideas are combined in complex ways to create a software product that embodies new knowledge. In this endeavor, software developers constantly look for actionable knowledge to help solve the problem at hand. While knowledge management efforts in the software development domain traditionally involved technical initiatives such as knowledge repositories, experience factories, and lessons-to-learn databases, there is a growing appreciation in the software community of the role of developers' personal knowledge networks in software development. However, research is scarce on the nature of these networks, the knowledge resources accessed from these networks, and the differences, if any, between developers of different experience levels. This research seeks to fill this void. Based on a case study in a software development organization, this research explores the nature of knowledge networks of developers from a social capital perspective. Specifically, it examines the structural and relational dimensions of developers' knowledge networks, identifies the specific actionable knowledge resources accessed from these networks, and explores how entry-level and more experienced developers differ along these dimensions. The findings from the qualitative analysis, backed by limited quantitative analysis of the case study data underpin the discussion, implications for practice and future research directions.

Agile Development Methods, considered as an alternative to the traditional plan-based methods, have received much attention since their inception. These practices have evolved and developed over time, culminating in 2001 with the Agile Manifesto. Since that time, preferred methodologies, implementations, and best practices have continued to evolve with a focus on doing what works best for the individual company or project. However, the concept of agility in software development has remained quite nebulous, lacking in clarity particularly about its underlying dimensions. In this research the authors conceive agility in terms of four distinct dimensions. Drawing from the theoretical perspective of holographic organization, they develop a model explaining how each of these underlying dimensions of agility contributes to project value in software teams. The authors test the model using survey data collected from industry practitioners and discuss findings. Copyright © 2017, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Ranking journals is an important exercise in academia. While several approaches to rank journals exist, an inherent assumption of these approaches is that there is indeed a hierarchy of journals, which is captured by the methods used for ranking them. We address a more fundamental question: Is there a linear hierarchy within journals? In this article, we introduce the dominance ranking approach that investigates the extent of hierarchy in a given set of objects by examining the extent of intransitivity in the system of interactions. We test the efficacy of the approach to ranking information systems journals based on citation data spanning a 3 year period from 2009 to 2011. Results indicate that the approach is very effective in identifying the extent of hierarchy within journals, and subsequently in ranking the journals. With its statistical underpinnings, the approach brings greater objectivity to the ranking of journals than prior approaches.

Hospitals have invested and continue to invest heavily in building information systems to support operations at various levels of administration. These systems generate a lot of data but fail to effectively convert these data into actionable information for decision makers. Such ineffectiveness often is attributed to a lack of alignment between strategic planning and information technology (TT) initiatives supporting operational goals. We present a case study that illustrates how the use of digital dashboards at St. Joseph Mercy Oakland (SJMO) Hospital in Pontiac, Michigan, was instrumental in supporting such an alignment. Driven by a focus on key performance indicators (KPIs), dashboard applications also led to other tangible and intangible benefits. An ability to track KPIs over time and against established targets, with drill-down capabilities, allowed leadership to hold staff members accountable for achieving their performance targets. By displaying the dashboards in prominent locations (such as operational unit floors, the physicians' cafeteria, and nursing stations), SJMO ushered in transparency in the planning and monitoring processes. The need to develop KPI metrics and drive data collection efforts became ingrained in the work ethos of people at every level of the organization. Although IT-enabled dashboards have been instrumental in supporting this cultural transformation, the focus of investment was the ability of technology to make collective vision and action the responsibility of all stakeholders.